Volunteer Summary

CONSORT Flow Diagram

Overall status

Characteristic

Overall1

Control1

Treatment1

time_point

1st

79

39

40

2nd

61

34

27

1n

Demographic information

Characteristic

N

Overall, N = 791

control, N = 391

treatment, N = 401

p-value2

age

79

40.90 ± 18.35 (21 - 148)

42.47 ± 21.27 (22 - 148)

39.36 ± 15.09 (21 - 70)

0.454

gender

79

0.283

female

57 (72%)

26 (67%)

31 (78%)

male

22 (28%)

13 (33%)

9 (22%)

occupation

79

0.815

civil

3 (3.8%)

2 (5.1%)

1 (2.5%)

clerk

16 (20%)

7 (18%)

9 (22%)

homemaker

7 (8.9%)

2 (5.1%)

5 (12%)

manager

10 (13%)

6 (15%)

4 (10%)

other

10 (13%)

4 (10%)

6 (15%)

professional

11 (14%)

8 (21%)

3 (7.5%)

retired

3 (3.8%)

1 (2.6%)

2 (5.0%)

service

4 (5.1%)

2 (5.1%)

2 (5.0%)

student

13 (16%)

6 (15%)

7 (18%)

unemploy

2 (2.5%)

1 (2.6%)

1 (2.5%)

working_status

79

54 (68%)

29 (74%)

25 (62%)

0.257

marital

79

0.885

divorced

3 (3.8%)

1 (2.6%)

2 (5.0%)

married

21 (27%)

11 (28%)

10 (25%)

single

54 (68%)

26 (67%)

28 (70%)

widowed

1 (1.3%)

1 (2.6%)

0 (0%)

marital_r

79

0.923

married

21 (27%)

11 (28%)

10 (25%)

other

4 (5.1%)

2 (5.1%)

2 (5.0%)

single

54 (68%)

26 (67%)

28 (70%)

education

79

0.038

primary

0 (0%)

0 (0%)

0 (0%)

secondary

11 (14%)

2 (5.1%)

9 (22%)

post-secondary

13 (16%)

9 (23%)

4 (10%)

university

55 (70%)

28 (72%)

27 (68%)

university_edu

79

55 (70%)

28 (72%)

27 (68%)

0.678

family_income

79

0.318

0_10000

10 (13%)

4 (10%)

6 (15%)

10001_20000

17 (22%)

5 (13%)

12 (30%)

20001_30000

13 (16%)

8 (21%)

5 (12%)

30001_40000

10 (13%)

5 (13%)

5 (12%)

40000_above

29 (37%)

17 (44%)

12 (30%)

high_income

79

39 (49%)

22 (56%)

17 (42%)

0.216

religion

79

0.662

buddhism

5 (6.3%)

4 (10%)

1 (2.5%)

catholic

5 (6.3%)

2 (5.1%)

3 (7.5%)

christianity

26 (33%)

12 (31%)

14 (35%)

nil

41 (52%)

21 (54%)

20 (50%)

other

1 (1.3%)

0 (0%)

1 (2.5%)

taoism

1 (1.3%)

0 (0%)

1 (2.5%)

religion_r

79

0.794

christianity

31 (39%)

14 (36%)

17 (42%)

nil

41 (52%)

21 (54%)

20 (50%)

other

7 (8.9%)

4 (10%)

3 (7.5%)

source

79

0.006

bokss

35 (44%)

14 (36%)

21 (52%)

facebook

12 (15%)

10 (26%)

2 (5.0%)

instagram

5 (6.3%)

5 (13%)

0 (0%)

other

13 (16%)

4 (10%)

9 (22%)

refresh

14 (18%)

6 (15%)

8 (20%)

1Mean ± SD (Range); n (%)

2Two Sample t-test; Pearson's Chi-squared test; Fisher's exact test

Measurement

Characteristic

N

Overall, N = 791

control, N = 391

treatment, N = 401

p-value2

sets

79

19.53 ± 2.29 (15 - 25)

19.18 ± 2.14 (15 - 24)

19.88 ± 2.40 (15 - 25)

0.178

setv

79

11.23 ± 1.69 (8 - 15)

11.03 ± 1.63 (8 - 14)

11.43 ± 1.75 (8 - 15)

0.298

maks

79

44.86 ± 3.93 (36 - 57)

44.26 ± 3.65 (36 - 52)

45.45 ± 4.14 (38 - 57)

0.179

ibs

79

15.66 ± 2.21 (9 - 20)

15.62 ± 2.14 (11 - 20)

15.70 ± 2.31 (9 - 20)

0.866

ers_e

79

12.30 ± 1.42 (9 - 15)

12.33 ± 1.46 (9 - 15)

12.28 ± 1.40 (9 - 15)

0.856

ers_r

79

11.41 ± 1.50 (8 - 15)

11.33 ± 1.36 (8 - 14)

11.47 ± 1.63 (8 - 15)

0.677

pss_pa

79

44.97 ± 4.67 (30 - 54)

44.41 ± 4.59 (30 - 54)

45.52 ± 4.74 (31 - 54)

0.292

pss_ps

79

25.49 ± 7.38 (12 - 42)

26.51 ± 7.71 (14 - 42)

24.50 ± 7.00 (12 - 41)

0.228

pss

79

43.52 ± 11.30 (21 - 72)

45.10 ± 11.69 (23 - 72)

41.98 ± 10.83 (21 - 67)

0.221

rki_responsible

79

21.27 ± 3.94 (13 - 29)

20.82 ± 4.25 (13 - 29)

21.70 ± 3.60 (14 - 28)

0.324

rki_nonlinear

79

13.44 ± 2.72 (7 - 22)

13.21 ± 2.48 (7 - 20)

13.68 ± 2.95 (8 - 22)

0.446

rki_peer

79

20.46 ± 2.20 (16 - 25)

20.54 ± 2.22 (16 - 25)

20.38 ± 2.20 (16 - 25)

0.744

rki_expect

79

4.70 ± 0.99 (3 - 7)

4.46 ± 0.94 (3 - 6)

4.92 ± 1.00 (3 - 7)

0.037

rki

79

59.86 ± 5.79 (50 - 80)

59.03 ± 5.89 (50 - 76)

60.67 ± 5.64 (50 - 80)

0.207

raq_possible

79

15.58 ± 1.88 (12 - 20)

15.64 ± 2.03 (12 - 20)

15.53 ± 1.74 (12 - 20)

0.786

raq_difficulty

79

12.30 ± 1.44 (9 - 15)

12.44 ± 1.48 (9 - 15)

12.18 ± 1.41 (9 - 15)

0.426

raq

79

27.89 ± 3.04 (21 - 35)

28.08 ± 3.26 (21 - 35)

27.70 ± 2.84 (21 - 35)

0.585

who

79

14.99 ± 4.42 (6 - 25)

14.95 ± 4.29 (8 - 25)

15.03 ± 4.59 (6 - 25)

0.939

phq

79

3.58 ± 3.78 (0 - 18)

3.72 ± 3.68 (0 - 14)

3.45 ± 3.92 (0 - 18)

0.755

gad

79

3.05 ± 3.11 (0 - 12)

3.28 ± 3.14 (0 - 12)

2.83 ± 3.11 (0 - 12)

0.518

nb_pcs

79

50.81 ± 7.80 (25 - 63)

51.43 ± 7.63 (25 - 63)

50.20 ± 8.01 (27 - 61)

0.488

nb_mcs

79

50.94 ± 8.63 (22 - 70)

50.39 ± 9.06 (22 - 68)

51.48 ± 8.27 (35 - 70)

0.577

1Mean ± SD (Range)

2Two Sample t-test

Data analysis

Table

Group

Characteristic

Beta

SE1

95% CI1

p-value

sets

(Intercept)

19.2

0.338

18.5, 19.8

group

control

—

—

—

treatment

0.696

0.475

-0.236, 1.63

0.146

time_point

1st

—

—

—

2nd

-0.296

0.399

-1.08, 0.486

0.461

group * time_point

treatment * 2nd

-0.011

0.589

-1.17, 1.14

0.985

Pseudo R square

0.032

setv

(Intercept)

11.0

0.270

10.5, 11.6

group

control

—

—

—

treatment

0.399

0.379

-0.344, 1.14

0.295

time_point

1st

—

—

—

2nd

0.260

0.268

-0.266, 0.786

0.336

group * time_point

treatment * 2nd

-0.193

0.398

-0.974, 0.587

0.629

Pseudo R square

0.011

maks

(Intercept)

44.3

0.649

43.0, 45.5

group

control

—

—

—

treatment

1.19

0.912

-0.594, 2.98

0.194

time_point

1st

—

—

—

2nd

0.068

0.496

-0.904, 1.04

0.892

group * time_point

treatment * 2nd

0.291

0.740

-1.16, 1.74

0.696

Pseudo R square

0.026

ibs

(Intercept)

15.6

0.337

15.0, 16.3

group

control

—

—

—

treatment

0.085

0.474

-0.845, 1.01

0.859

time_point

1st

—

—

—

2nd

0.189

0.317

-0.432, 0.810

0.553

group * time_point

treatment * 2nd

0.259

0.471

-0.664, 1.18

0.584

Pseudo R square

0.008

ers_e

(Intercept)

12.3

0.228

11.9, 12.8

group

control

—

—

—

treatment

-0.058

0.320

-0.686, 0.569

0.856

time_point

1st

—

—

—

2nd

-0.532

0.185

-0.895, -0.169

0.006

group * time_point

treatment * 2nd

0.528

0.276

-0.014, 1.07

0.061

Pseudo R square

0.022

ers_r

(Intercept)

11.3

0.234

10.9, 11.8

group

control

—

—

—

treatment

0.142

0.329

-0.503, 0.786

0.668

time_point

1st

—

—

—

2nd

-0.125

0.256

-0.626, 0.376

0.626

group * time_point

treatment * 2nd

0.268

0.378

-0.473, 1.01

0.481

Pseudo R square

0.010

pss_pa

(Intercept)

44.4

0.731

43.0, 45.8

group

control

—

—

—

treatment

1.11

1.027

-0.899, 3.13

0.280

time_point

1st

—

—

—

2nd

-1.39

0.802

-2.96, 0.185

0.089

group * time_point

treatment * 2nd

0.101

1.187

-2.22, 2.43

0.932

Pseudo R square

0.038

pss_ps

(Intercept)

26.5

1.174

24.2, 28.8

group

control

—

—

—

treatment

-2.01

1.650

-5.25, 1.22

0.225

time_point

1st

—

—

—

2nd

1.23

1.119

-0.966, 3.42

0.277

group * time_point

treatment * 2nd

-1.37

1.663

-4.63, 1.89

0.413

Pseudo R square

0.035

pss

(Intercept)

45.1

1.752

41.7, 48.5

group

control

—

—

—

treatment

-3.13

2.463

-7.95, 1.70

0.207

time_point

1st

—

—

—

2nd

2.65

1.637

-0.557, 5.86

0.110

group * time_point

treatment * 2nd

-1.52

2.434

-6.29, 3.25

0.534

Pseudo R square

0.039

rki_responsible

(Intercept)

20.8

0.590

19.7, 22.0

group

control

—

—

—

treatment

0.879

0.829

-0.744, 2.50

0.291

time_point

1st

—

—

—

2nd

0.047

0.602

-1.13, 1.23

0.938

group * time_point

treatment * 2nd

-0.394

0.893

-2.14, 1.36

0.661

Pseudo R square

0.010

rki_nonlinear

(Intercept)

13.2

0.449

12.3, 14.1

group

control

—

—

—

treatment

0.470

0.632

-0.768, 1.71

0.459

time_point

1st

—

—

—

2nd

-0.340

0.439

-1.20, 0.520

0.441

group * time_point

treatment * 2nd

0.528

0.651

-0.749, 1.80

0.421

Pseudo R square

0.018

rki_peer

(Intercept)

20.5

0.360

19.8, 21.2

group

control

—

—

—

treatment

-0.163

0.506

-1.15, 0.828

0.747

time_point

1st

—

—

—

2nd

-0.036

0.363

-0.747, 0.675

0.921

group * time_point

treatment * 2nd

0.219

0.538

-0.836, 1.27

0.686

Pseudo R square

0.001

rki_expect

(Intercept)

4.46

0.153

4.16, 4.76

group

control

—

—

—

treatment

0.463

0.215

0.042, 0.885

0.033

time_point

1st

—

—

—

2nd

0.161

0.194

-0.219, 0.542

0.409

group * time_point

treatment * 2nd

-0.003

0.286

-0.563, 0.557

0.992

Pseudo R square

0.059

rki

(Intercept)

59.0

0.868

57.3, 60.7

group

control

—

—

—

treatment

1.65

1.220

-0.743, 4.04

0.179

time_point

1st

—

—

—

2nd

-0.148

0.911

-1.93, 1.64

0.872

group * time_point

treatment * 2nd

0.296

1.349

-2.35, 2.94

0.827

Pseudo R square

0.026

raq_possible

(Intercept)

15.6

0.288

15.1, 16.2

group

control

—

—

—

treatment

-0.116

0.404

-0.908, 0.676

0.775

time_point

1st

—

—

—

2nd

-0.358

0.314

-0.973, 0.256

0.257

group * time_point

treatment * 2nd

0.694

0.464

-0.215, 1.60

0.139

Pseudo R square

0.012

raq_difficulty

(Intercept)

12.4

0.231

12.0, 12.9

group

control

—

—

—

treatment

-0.261

0.324

-0.897, 0.375

0.423

time_point

1st

—

—

—

2nd

-0.015

0.218

-0.442, 0.412

0.944

group * time_point

treatment * 2nd

0.212

0.324

-0.422, 0.847

0.515

Pseudo R square

0.006

raq

(Intercept)

28.1

0.479

27.1, 29.0

group

control

—

—

—

treatment

-0.377

0.673

-1.70, 0.941

0.576

time_point

1st

—

—

—

2nd

-0.351

0.465

-1.26, 0.561

0.454

group * time_point

treatment * 2nd

0.920

0.691

-0.434, 2.27

0.187

Pseudo R square

0.006

who

(Intercept)

14.9

0.706

13.6, 16.3

group

control

—

—

—

treatment

0.076

0.992

-1.87, 2.02

0.939

time_point

1st

—

—

—

2nd

-0.316

0.560

-1.41, 0.782

0.575

group * time_point

treatment * 2nd

0.047

0.836

-1.59, 1.68

0.955

Pseudo R square

0.001

phq

(Intercept)

3.72

0.579

2.58, 4.85

group

control

—

—

—

treatment

-0.268

0.813

-1.86, 1.33

0.743

time_point

1st

—

—

—

2nd

0.059

0.380

-0.686, 0.805

0.877

group * time_point

treatment * 2nd

-0.067

0.569

-1.18, 1.05

0.906

Pseudo R square

0.002

gad

(Intercept)

3.28

0.508

2.29, 4.28

group

control

—

—

—

treatment

-0.457

0.714

-1.86, 0.943

0.524

time_point

1st

—

—

—

2nd

0.214

0.415

-0.599, 1.03

0.608

group * time_point

treatment * 2nd

0.143

0.618

-1.07, 1.35

0.818

Pseudo R square

0.006

nb_pcs

(Intercept)

51.4

1.198

49.1, 53.8

group

control

—

—

—

treatment

-1.23

1.684

-4.53, 2.07

0.468

time_point

1st

—

—

—

2nd

-0.608

0.881

-2.33, 1.12

0.493

group * time_point

treatment * 2nd

2.33

1.315

-0.245, 4.91

0.081

Pseudo R square

0.007

nb_mcs

(Intercept)

50.4

1.339

47.8, 53.0

group

control

—

—

—

treatment

1.09

1.881

-2.60, 4.78

0.563

time_point

1st

—

—

—

2nd

-0.203

1.224

-2.60, 2.20

0.869

group * time_point

treatment * 2nd

-0.735

1.821

-4.30, 2.83

0.688

Pseudo R square

0.004

1SE = Standard Error, CI = Confidence Interval

Text

sets

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict sets with group and time_point (formula: sets ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.39) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 19.18 (95% CI [18.52, 19.84], t(134) = 56.74, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.70, 95% CI [-0.24, 1.63], t(134) = 1.46, p = 0.143; Std. beta = 0.33, 95% CI [-0.11, 0.77])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.30, 95% CI [-1.08, 0.49], t(134) = -0.74, p = 0.458; Std. beta = -0.14, 95% CI [-0.51, 0.23])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.01, 95% CI [-1.17, 1.14], t(134) = -0.02, p = 0.985; Std. beta = -5.28e-03, 95% CI [-0.55, 0.54])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

setv

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict setv with group and time_point (formula: setv ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.56) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 11.03 (95% CI [10.50, 11.55], t(134) = 40.84, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.40, 95% CI [-0.34, 1.14], t(134) = 1.05, p = 0.293; Std. beta = 0.24, 95% CI [-0.21, 0.68])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.26, 95% CI [-0.27, 0.79], t(134) = 0.97, p = 0.332; Std. beta = 0.16, 95% CI [-0.16, 0.47])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.19, 95% CI [-0.97, 0.59], t(134) = -0.49, p = 0.627; Std. beta = -0.12, 95% CI [-0.58, 0.35])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

maks

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict maks with group and time_point (formula: maks ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.75) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 44.26 (95% CI [42.98, 45.53], t(134) = 68.21, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 1.19, 95% CI [-0.59, 2.98], t(134) = 1.31, p = 0.191; Std. beta = 0.30, 95% CI [-0.15, 0.75])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.07, 95% CI [-0.90, 1.04], t(134) = 0.14, p = 0.892; Std. beta = 0.02, 95% CI [-0.23, 0.26])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.29, 95% CI [-1.16, 1.74], t(134) = 0.39, p = 0.694; Std. beta = 0.07, 95% CI [-0.29, 0.44])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

ibs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ibs with group and time_point (formula: ibs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.61) and the part related to the fixed effects alone (marginal R2) is of 8.01e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 15.62 (95% CI [14.95, 16.28], t(134) = 46.28, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.08, 95% CI [-0.84, 1.01], t(134) = 0.18, p = 0.858; Std. beta = 0.04, 95% CI [-0.41, 0.49])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.19, 95% CI [-0.43, 0.81], t(134) = 0.60, p = 0.551; Std. beta = 0.09, 95% CI [-0.21, 0.39])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.26, 95% CI [-0.66, 1.18], t(134) = 0.55, p = 0.582; Std. beta = 0.12, 95% CI [-0.32, 0.57])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

ers_e

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ers_e with group and time_point (formula: ers_e ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.71) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 12.33 (95% CI [11.89, 12.78], t(134) = 54.15, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.06, 95% CI [-0.69, 0.57], t(134) = -0.18, p = 0.855; Std. beta = -0.04, 95% CI [-0.49, 0.41])
  • The effect of time point [2nd] is statistically significant and negative (beta = -0.53, 95% CI [-0.90, -0.17], t(134) = -2.87, p = 0.004; Std. beta = -0.38, 95% CI [-0.64, -0.12])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.53, 95% CI [-0.01, 1.07], t(134) = 1.91, p = 0.056; Std. beta = 0.38, 95% CI [-9.71e-03, 0.76])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

ers_r

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ers_r with group and time_point (formula: ers_r ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.47) and the part related to the fixed effects alone (marginal R2) is of 9.78e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 11.33 (95% CI [10.87, 11.79], t(134) = 48.41, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.14, 95% CI [-0.50, 0.79], t(134) = 0.43, p = 0.667; Std. beta = 0.10, 95% CI [-0.35, 0.55])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.13, 95% CI [-0.63, 0.38], t(134) = -0.49, p = 0.624; Std. beta = -0.09, 95% CI [-0.43, 0.26])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.27, 95% CI [-0.47, 1.01], t(134) = 0.71, p = 0.478; Std. beta = 0.19, 95% CI [-0.33, 0.70])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

pss_pa

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict pss_pa with group and time_point (formula: pss_pa ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.48) and the part related to the fixed effects alone (marginal R2) is of 0.04. The model’s intercept, corresponding to group = control and time_point = 1st, is at 44.41 (95% CI [42.98, 45.84], t(134) = 60.74, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 1.11, 95% CI [-0.90, 3.13], t(134) = 1.08, p = 0.278; Std. beta = 0.24, 95% CI [-0.20, 0.68])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -1.39, 95% CI [-2.96, 0.18], t(134) = -1.73, p = 0.084; Std. beta = -0.30, 95% CI [-0.64, 0.04])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.10, 95% CI [-2.22, 2.43], t(134) = 0.09, p = 0.932; Std. beta = 0.02, 95% CI [-0.48, 0.53])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

pss_ps

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict pss_ps with group and time_point (formula: pss_ps ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.61) and the part related to the fixed effects alone (marginal R2) is of 0.04. The model’s intercept, corresponding to group = control and time_point = 1st, is at 26.51 (95% CI [24.21, 28.81], t(134) = 22.59, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -2.01, 95% CI [-5.25, 1.22], t(134) = -1.22, p = 0.222; Std. beta = -0.27, 95% CI [-0.71, 0.17])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 1.23, 95% CI [-0.97, 3.42], t(134) = 1.10, p = 0.273; Std. beta = 0.17, 95% CI [-0.13, 0.46])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -1.37, 95% CI [-4.63, 1.89], t(134) = -0.82, p = 0.410; Std. beta = -0.19, 95% CI [-0.63, 0.26])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

pss

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict pss with group and time_point (formula: pss ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.63) and the part related to the fixed effects alone (marginal R2) is of 0.04. The model’s intercept, corresponding to group = control and time_point = 1st, is at 45.10 (95% CI [41.67, 48.54], t(134) = 25.74, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -3.13, 95% CI [-7.95, 1.70], t(134) = -1.27, p = 0.204; Std. beta = -0.28, 95% CI [-0.72, 0.15])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 2.65, 95% CI [-0.56, 5.86], t(134) = 1.62, p = 0.105; Std. beta = 0.24, 95% CI [-0.05, 0.53])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -1.52, 95% CI [-6.29, 3.25], t(134) = -0.63, p = 0.532; Std. beta = -0.14, 95% CI [-0.57, 0.29])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_responsible

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_responsible with group and time_point (formula: rki_responsible ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.54) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 20.82 (95% CI [19.66, 21.98], t(134) = 35.31, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.88, 95% CI [-0.74, 2.50], t(134) = 1.06, p = 0.288; Std. beta = 0.24, 95% CI [-0.21, 0.69])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.05, 95% CI [-1.13, 1.23], t(134) = 0.08, p = 0.937; Std. beta = 0.01, 95% CI [-0.31, 0.34])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.39, 95% CI [-2.14, 1.36], t(134) = -0.44, p = 0.659; Std. beta = -0.11, 95% CI [-0.59, 0.38])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_nonlinear

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_nonlinear with group and time_point (formula: rki_nonlinear ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.58) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 13.21 (95% CI [12.32, 14.09], t(134) = 29.38, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.47, 95% CI [-0.77, 1.71], t(134) = 0.74, p = 0.457; Std. beta = 0.17, 95% CI [-0.28, 0.62])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.34, 95% CI [-1.20, 0.52], t(134) = -0.78, p = 0.438; Std. beta = -0.12, 95% CI [-0.44, 0.19])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.53, 95% CI [-0.75, 1.80], t(134) = 0.81, p = 0.418; Std. beta = 0.19, 95% CI [-0.27, 0.65])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_peer

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_peer with group and time_point (formula: rki_peer ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.54) and the part related to the fixed effects alone (marginal R2) is of 1.06e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 20.54 (95% CI [19.83, 21.24], t(134) = 57.06, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.16, 95% CI [-1.15, 0.83], t(134) = -0.32, p = 0.747; Std. beta = -0.07, 95% CI [-0.52, 0.37])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.04, 95% CI [-0.75, 0.67], t(134) = -0.10, p = 0.921; Std. beta = -0.02, 95% CI [-0.34, 0.30])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.22, 95% CI [-0.84, 1.27], t(134) = 0.41, p = 0.685; Std. beta = 0.10, 95% CI [-0.38, 0.57])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_expect

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_expect with group and time_point (formula: rki_expect ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.31) and the part related to the fixed effects alone (marginal R2) is of 0.06. The model’s intercept, corresponding to group = control and time_point = 1st, is at 4.46 (95% CI [4.16, 4.76], t(134) = 29.16, p < .001). Within this model:

  • The effect of group [treatment] is statistically significant and positive (beta = 0.46, 95% CI [0.04, 0.88], t(134) = 2.16, p = 0.031; Std. beta = 0.48, 95% CI [0.04, 0.91])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.16, 95% CI [-0.22, 0.54], t(134) = 0.83, p = 0.406; Std. beta = 0.17, 95% CI [-0.22, 0.56])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -2.89e-03, 95% CI [-0.56, 0.56], t(134) = -0.01, p = 0.992; Std. beta = -2.96e-03, 95% CI [-0.58, 0.57])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki with group and time_point (formula: rki ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.52) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 59.03 (95% CI [57.32, 60.73], t(134) = 67.97, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 1.65, 95% CI [-0.74, 4.04], t(134) = 1.35, p = 0.177; Std. beta = 0.31, 95% CI [-0.14, 0.76])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.15, 95% CI [-1.93, 1.64], t(134) = -0.16, p = 0.871; Std. beta = -0.03, 95% CI [-0.36, 0.31])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.30, 95% CI [-2.35, 2.94], t(134) = 0.22, p = 0.826; Std. beta = 0.06, 95% CI [-0.44, 0.55])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

raq_possible

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict raq_possible with group and time_point (formula: raq_possible ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.47) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 15.64 (95% CI [15.08, 16.20], t(134) = 54.40, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.12, 95% CI [-0.91, 0.68], t(134) = -0.29, p = 0.774; Std. beta = -0.06, 95% CI [-0.51, 0.38])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.36, 95% CI [-0.97, 0.26], t(134) = -1.14, p = 0.253; Std. beta = -0.20, 95% CI [-0.54, 0.14])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.69, 95% CI [-0.21, 1.60], t(134) = 1.50, p = 0.134; Std. beta = 0.39, 95% CI [-0.12, 0.90])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

raq_difficulty

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict raq_difficulty with group and time_point (formula: raq_difficulty ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.60) and the part related to the fixed effects alone (marginal R2) is of 5.82e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 12.44 (95% CI [11.98, 12.89], t(134) = 53.86, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.26, 95% CI [-0.90, 0.38], t(134) = -0.80, p = 0.421; Std. beta = -0.18, 95% CI [-0.62, 0.26])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.02, 95% CI [-0.44, 0.41], t(134) = -0.07, p = 0.944; Std. beta = -0.01, 95% CI [-0.31, 0.29])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.21, 95% CI [-0.42, 0.85], t(134) = 0.66, p = 0.512; Std. beta = 0.15, 95% CI [-0.29, 0.59])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

raq

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict raq with group and time_point (formula: raq ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.58) and the part related to the fixed effects alone (marginal R2) is of 5.98e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 28.08 (95% CI [27.14, 29.01], t(134) = 58.67, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.38, 95% CI [-1.70, 0.94], t(134) = -0.56, p = 0.575; Std. beta = -0.13, 95% CI [-0.57, 0.32])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.35, 95% CI [-1.26, 0.56], t(134) = -0.75, p = 0.451; Std. beta = -0.12, 95% CI [-0.42, 0.19])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.92, 95% CI [-0.43, 2.27], t(134) = 1.33, p = 0.183; Std. beta = 0.31, 95% CI [-0.15, 0.76])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

who

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict who with group and time_point (formula: who ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.72) and the part related to the fixed effects alone (marginal R2) is of 1.28e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 14.95 (95% CI [13.57, 16.33], t(134) = 21.18, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.08, 95% CI [-1.87, 2.02], t(134) = 0.08, p = 0.939; Std. beta = 0.02, 95% CI [-0.43, 0.47])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.32, 95% CI [-1.41, 0.78], t(134) = -0.56, p = 0.573; Std. beta = -0.07, 95% CI [-0.33, 0.18])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.05, 95% CI [-1.59, 1.68], t(134) = 0.06, p = 0.955; Std. beta = 0.01, 95% CI [-0.37, 0.39])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

phq

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict phq with group and time_point (formula: phq ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.81) and the part related to the fixed effects alone (marginal R2) is of 1.75e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.72 (95% CI [2.58, 4.85], t(134) = 6.42, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.27, 95% CI [-1.86, 1.33], t(134) = -0.33, p = 0.742; Std. beta = -0.07, 95% CI [-0.52, 0.37])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.06, 95% CI [-0.69, 0.80], t(134) = 0.16, p = 0.876; Std. beta = 0.02, 95% CI [-0.19, 0.22])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.07, 95% CI [-1.18, 1.05], t(134) = -0.12, p = 0.906; Std. beta = -0.02, 95% CI [-0.33, 0.29])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

gad

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict gad with group and time_point (formula: gad ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.71) and the part related to the fixed effects alone (marginal R2) is of 6.27e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.28 (95% CI [2.29, 4.28], t(134) = 6.46, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.46, 95% CI [-1.86, 0.94], t(134) = -0.64, p = 0.522; Std. beta = -0.14, 95% CI [-0.59, 0.30])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.21, 95% CI [-0.60, 1.03], t(134) = 0.52, p = 0.606; Std. beta = 0.07, 95% CI [-0.19, 0.32])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.14, 95% CI [-1.07, 1.35], t(134) = 0.23, p = 0.817; Std. beta = 0.05, 95% CI [-0.34, 0.43])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_pcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_pcs with group and time_point (formula: nb_pcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.76) and the part related to the fixed effects alone (marginal R2) is of 7.25e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 51.43 (95% CI [49.08, 53.78], t(134) = 42.92, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -1.23, 95% CI [-4.53, 2.07], t(134) = -0.73, p = 0.466; Std. beta = -0.16, 95% CI [-0.61, 0.28])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.61, 95% CI [-2.33, 1.12], t(134) = -0.69, p = 0.490; Std. beta = -0.08, 95% CI [-0.31, 0.15])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 2.33, 95% CI [-0.24, 4.91], t(134) = 1.77, p = 0.076; Std. beta = 0.31, 95% CI [-0.03, 0.66])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_mcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_mcs with group and time_point (formula: nb_mcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.63) and the part related to the fixed effects alone (marginal R2) is of 3.87e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 50.39 (95% CI [47.77, 53.02], t(134) = 37.64, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 1.09, 95% CI [-2.60, 4.78], t(134) = 0.58, p = 0.562; Std. beta = 0.13, 95% CI [-0.31, 0.58])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.20, 95% CI [-2.60, 2.20], t(134) = -0.17, p = 0.868; Std. beta = -0.02, 95% CI [-0.31, 0.26])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.73, 95% CI [-4.30, 2.83], t(134) = -0.40, p = 0.686; Std. beta = -0.09, 95% CI [-0.52, 0.34])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

Likelihood ratio tests

outcome

model

npar

AIC

BIC

logLik

deviance

Chisq

Df

p

sets

null

3

603.804

612.629

-298.902

597.804

sets

random

6

605.636

623.286

-296.818

593.636

4.168

3

0.244

setv

null

3

524.662

533.487

-259.331

518.662

setv

random

6

528.838

546.488

-258.419

516.838

1.823

3

0.610

maks

null

3

745.149

753.974

-369.574

739.149

maks

random

6

748.420

766.070

-368.210

736.420

2.728

3

0.435

ibs

null

3

582.183

591.008

-288.092

576.183

ibs

random

6

586.035

603.685

-287.017

574.035

2.148

3

0.542

ers_e

null

3

464.134

472.959

-229.067

458.134

ers_e

random

6

461.790

479.440

-224.895

449.790

8.344

3

0.039

ers_r

null

3

492.254

501.079

-243.127

486.254

ers_r

random

6

496.971

514.621

-242.486

484.971

1.282

3

0.733

pss_pa

null

3

817.268

826.093

-405.634

811.268

pss_pa

random

6

816.198

833.848

-402.099

804.198

7.070

3

0.070

pss_ps

null

3

934.874

943.699

-464.437

928.874

pss_ps

random

6

936.599

954.248

-462.299

924.599

4.276

3

0.233

pss

null

3

1,046.909

1,055.734

-520.455

1,040.909

pss

random

6

1,046.865

1,064.515

-517.432

1,034.865

6.044

3

0.109

rki_responsible

null

3

745.252

754.077

-369.626

739.252

rki_responsible

random

6

749.976

767.626

-368.988

737.976

1.276

3

0.735

rki_nonlinear

null

3

666.150

674.975

-330.075

660.150

rki_nonlinear

random

6

669.905

687.555

-328.953

657.905

2.245

3

0.523

rki_peer

null

3

604.907

613.732

-299.453

598.907

rki_peer

random

6

610.648

628.298

-299.324

598.648

0.258

3

0.968

rki_expect

null

3

389.677

398.502

-191.838

383.677

rki_expect

random

6

388.097

405.747

-188.049

376.097

7.580

3

0.056

rki

null

3

857.411

866.236

-425.705

851.411

rki

random

6

860.676

878.326

-424.338

848.676

2.735

3

0.434

raq_possible

null

3

550.938

559.763

-272.469

544.938

raq_possible

random

6

554.404

572.054

-271.202

542.404

2.534

3

0.469

raq_difficulty

null

3

475.368

484.193

-234.684

469.368

raq_difficulty

random

6

480.282

497.932

-234.141

468.282

1.086

3

0.780

raq

null

3

682.981

691.805

-338.490

676.981

raq

random

6

687.141

704.791

-337.570

675.141

1.840

3

0.606

who

null

3

770.469

779.294

-382.234

764.469

who

random

6

775.930

793.580

-381.965

763.930

0.539

3

0.910

phq

null

3

694.249

703.074

-344.125

688.249

phq

random

6

700.076

717.726

-344.038

688.076

0.173

3

0.982

gad

null

3

682.188

691.013

-338.094

676.188

gad

random

6

686.884

704.534

-337.442

674.884

1.303

3

0.728

nb_pcs

null

3

913.648

922.473

-453.824

907.648

nb_pcs

random

6

916.062

933.712

-452.031

904.062

3.586

3

0.310

nb_mcs

null

3

964.203

973.028

-479.102

958.203

nb_mcs

random

6

969.435

987.084

-478.717

957.435

0.769

3

0.857

Post hoc analysis text

Table

outcome

time

control

treatment

between

n

estimate

within es

n

estimate

within es

p

es

sets

1st

39

19.18 ± 2.11

40

19.87 ± 2.11

0.146

-0.414

sets

2nd

34

18.88 ± 2.10

0.176

27

19.57 ± 2.07

0.183

0.205

-0.407

setv

1st

39

11.03 ± 1.69

40

11.42 ± 1.69

0.295

-0.356

setv

2nd

34

11.29 ± 1.65

-0.232

27

11.49 ± 1.60

-0.060

0.624

-0.184

maks

1st

39

44.26 ± 4.05

40

45.45 ± 4.05

0.194

-0.579

maks

2nd

34

44.32 ± 3.91

-0.033

27

45.81 ± 3.68

-0.174

0.131

-0.720

ibs

1st

39

15.62 ± 2.11

40

15.70 ± 2.11

0.859

-0.064

ibs

2nd

34

15.80 ± 2.06

-0.143

27

16.15 ± 1.98

-0.339

0.510

-0.260

ers_e

1st

39

12.33 ± 1.42

40

12.28 ± 1.42

0.856

0.076

ers_e

2nd

34

11.80 ± 1.38

0.690

27

12.27 ± 1.30

0.006

0.176

-0.609

ers_r

1st

39

11.33 ± 1.46

40

11.48 ± 1.46

0.668

-0.132

ers_r

2nd

34

11.21 ± 1.44

0.117

27

11.62 ± 1.42

-0.133

0.268

-0.382

pss_pa

1st

39

44.41 ± 4.57

40

45.53 ± 4.57

0.280

-0.331

pss_pa

2nd

34

43.02 ± 4.51

0.412

27

44.24 ± 4.42

0.382

0.293

-0.361

pss_ps

1st

39

26.51 ± 7.33

40

24.50 ± 7.33

0.225

0.430

pss_ps

2nd

34

27.74 ± 7.17

-0.263

27

24.36 ± 6.92

0.031

0.064

0.724

pss

1st

39

45.10 ± 10.94

40

41.97 ± 10.94

0.207

0.457

pss

2nd

34

47.75 ± 10.69

-0.388

27

43.11 ± 10.29

-0.165

0.087

0.680

rki_responsible

1st

39

20.82 ± 3.68

40

21.70 ± 3.68

0.291

-0.349

rki_responsible

2nd

34

20.87 ± 3.62

-0.019

27

21.35 ± 3.52

0.138

0.598

-0.193

rki_nonlinear

1st

39

13.21 ± 2.81

40

13.68 ± 2.81

0.459

-0.256

rki_nonlinear

2nd

34

12.87 ± 2.75

0.185

27

13.86 ± 2.66

-0.102

0.154

-0.544

rki_peer

1st

39

20.54 ± 2.25

40

20.37 ± 2.25

0.747

0.108

rki_peer

2nd

34

20.50 ± 2.21

0.024

27

20.56 ± 2.14

-0.120

0.922

-0.036

rki_expect

1st

39

4.46 ± 0.96

40

4.92 ± 0.96

0.033

-0.565

rki_expect

2nd

34

4.62 ± 0.95

-0.197

27

5.08 ± 0.95

-0.193

0.062

-0.562

rki

1st

39

59.03 ± 5.42

40

60.68 ± 5.42

0.179

-0.432

rki

2nd

34

58.88 ± 5.34

0.039

27

60.82 ± 5.21

-0.039

0.154

-0.510

raq_possible

1st

39

15.64 ± 1.80

40

15.52 ± 1.80

0.775

0.088

raq_possible

2nd

34

15.28 ± 1.77

0.272

27

15.86 ± 1.74

-0.256

0.203

-0.440

raq_difficulty

1st

39

12.44 ± 1.44

40

12.18 ± 1.44

0.423

0.287

raq_difficulty

2nd

34

12.42 ± 1.41

0.017

27

12.37 ± 1.36

-0.216

0.891

0.054

raq

1st

39

28.08 ± 2.99

40

27.70 ± 2.99

0.576

0.194

raq

2nd

34

27.73 ± 2.93

0.180

27

28.27 ± 2.83

-0.293

0.465

-0.279

who

1st

39

14.95 ± 4.41

40

15.03 ± 4.41

0.939

-0.033

who

2nd

34

14.63 ± 4.26

0.136

27

14.76 ± 4.02

0.116

0.908

-0.053

phq

1st

39

3.72 ± 3.61

40

3.45 ± 3.61

0.743

0.170

phq

2nd

34

3.78 ± 3.46

-0.038

27

3.44 ± 3.21

0.005

0.696

0.213

gad

1st

39

3.28 ± 3.17

40

2.82 ± 3.17

0.524

0.265

gad

2nd

34

3.50 ± 3.07

-0.124

27

3.18 ± 2.91

-0.206

0.684

0.182

nb_pcs

1st

39

51.43 ± 7.48

40

50.20 ± 7.48

0.468

0.335

nb_pcs

2nd

34

50.82 ± 7.20

0.166

27

51.93 ± 6.75

-0.471

0.539

-0.302

nb_mcs

1st

39

50.39 ± 8.36

40

51.48 ± 8.36

0.563

-0.214

nb_mcs

2nd

34

50.19 ± 8.15

0.040

27

50.55 ± 7.83

0.184

0.863

-0.070

Between group

sets

1st

t(121.99) = 1.46, p = 0.146, Cohen d = -0.41, 95% CI (-0.24 to 1.64)

2st

t(132.06) = 1.27, p = 0.205, Cohen d = -0.41, 95% CI (-0.38 to 1.75)

setv

1st

t(108.04) = 1.05, p = 0.295, Cohen d = -0.36, 95% CI (-0.35 to 1.15)

2st

t(124.70) = 0.49, p = 0.624, Cohen d = -0.18, 95% CI (-0.62 to 1.04)

maks

1st

t(94.17) = 1.31, p = 0.194, Cohen d = -0.58, 95% CI (-0.62 to 3.00)

2st

t(110.80) = 1.52, p = 0.131, Cohen d = -0.72, 95% CI (-0.45 to 3.42)

ibs

1st

t(104.33) = 0.18, p = 0.859, Cohen d = -0.06, 95% CI (-0.86 to 1.02)

2st

t(121.81) = 0.66, p = 0.510, Cohen d = -0.26, 95% CI (-0.69 to 1.37)

ers_e

1st

t(96.74) = -0.18, p = 0.856, Cohen d = 0.08, 95% CI (-0.69 to 0.58)

2st

t(114.08) = 1.36, p = 0.176, Cohen d = -0.61, 95% CI (-0.21 to 1.15)

ers_r

1st

t(115.29) = 0.43, p = 0.668, Cohen d = -0.13, 95% CI (-0.51 to 0.79)

2st

t(129.10) = 1.11, p = 0.268, Cohen d = -0.38, 95% CI (-0.32 to 1.14)

pss_pa

1st

t(115.65) = 1.08, p = 0.280, Cohen d = -0.33, 95% CI (-0.92 to 3.15)

2st

t(129.28) = 1.06, p = 0.293, Cohen d = -0.36, 95% CI (-1.06 to 3.49)

pss_ps

1st

t(105.30) = -1.22, p = 0.225, Cohen d = 0.43, 95% CI (-5.28 to 1.26)

2st

t(122.61) = -1.87, p = 0.064, Cohen d = 0.72, 95% CI (-6.97 to 0.20)

pss

1st

t(104.02) = -1.27, p = 0.207, Cohen d = 0.46, 95% CI (-8.01 to 1.76)

2st

t(121.54) = -1.72, p = 0.087, Cohen d = 0.68, 95% CI (-9.99 to 0.69)

rki_responsible

1st

t(110.02) = 1.06, p = 0.291, Cohen d = -0.35, 95% CI (-0.76 to 2.52)

2st

t(126.05) = 0.53, p = 0.598, Cohen d = -0.19, 95% CI (-1.33 to 2.30)

rki_nonlinear

1st

t(106.82) = 0.74, p = 0.459, Cohen d = -0.26, 95% CI (-0.78 to 1.72)

2st

t(123.80) = 1.43, p = 0.154, Cohen d = -0.54, 95% CI (-0.38 to 2.37)

rki_peer

1st

t(109.06) = -0.32, p = 0.747, Cohen d = 0.11, 95% CI (-1.17 to 0.84)

2st

t(125.41) = 0.10, p = 0.922, Cohen d = -0.04, 95% CI (-1.05 to 1.16)

rki_expect

1st

t(128.36) = 2.16, p = 0.033, Cohen d = -0.57, 95% CI (0.04 to 0.89)

2st

t(134.15) = 1.88, p = 0.062, Cohen d = -0.56, 95% CI (-0.02 to 0.95)

rki

1st

t(112.02) = 1.35, p = 0.179, Cohen d = -0.43, 95% CI (-0.77 to 4.07)

2st

t(127.30) = 1.43, p = 0.154, Cohen d = -0.51, 95% CI (-0.74 to 4.63)

raq_possible

1st

t(115.14) = -0.29, p = 0.775, Cohen d = 0.09, 95% CI (-0.92 to 0.68)

2st

t(129.03) = 1.28, p = 0.203, Cohen d = -0.44, 95% CI (-0.32 to 1.47)

raq_difficulty

1st

t(104.64) = -0.80, p = 0.423, Cohen d = 0.29, 95% CI (-0.90 to 0.38)

2st

t(122.06) = -0.14, p = 0.891, Cohen d = 0.05, 95% CI (-0.75 to 0.66)

raq

1st

t(106.56) = -0.56, p = 0.576, Cohen d = 0.19, 95% CI (-1.71 to 0.96)

2st

t(123.61) = 0.73, p = 0.465, Cohen d = -0.28, 95% CI (-0.92 to 2.01)

who

1st

t(95.70) = 0.08, p = 0.939, Cohen d = -0.03, 95% CI (-1.89 to 2.04)

2st

t(112.80) = 0.12, p = 0.908, Cohen d = -0.05, 95% CI (-1.99 to 2.23)

phq

1st

t(89.29) = -0.33, p = 0.743, Cohen d = 0.17, 95% CI (-1.88 to 1.35)

2st

t(103.42) = -0.39, p = 0.696, Cohen d = 0.21, 95% CI (-2.03 to 1.36)

gad

1st

t(96.90) = -0.64, p = 0.524, Cohen d = 0.26, 95% CI (-1.87 to 0.96)

2st

t(114.27) = -0.41, p = 0.684, Cohen d = 0.18, 95% CI (-1.84 to 1.21)

nb_pcs

1st

t(92.74) = -0.73, p = 0.468, Cohen d = 0.34, 95% CI (-4.57 to 2.12)

2st

t(108.80) = 0.62, p = 0.539, Cohen d = -0.30, 95% CI (-2.45 to 4.66)

nb_mcs

1st

t(102.74) = 0.58, p = 0.563, Cohen d = -0.21, 95% CI (-2.64 to 4.82)

2st

t(120.40) = 0.17, p = 0.863, Cohen d = -0.07, 95% CI (-3.71 to 4.43)

Within treatment group

sets

1st vs 2st

t(71.55) = -0.71, p = 0.482, Cohen d = 0.18, 95% CI (-1.17 to 0.56)

setv

1st vs 2st

t(67.82) = 0.23, p = 0.821, Cohen d = -0.06, 95% CI (-0.52 to 0.66)

maks

1st vs 2st

t(64.11) = 0.65, p = 0.517, Cohen d = -0.17, 95% CI (-0.74 to 1.46)

ibs

1st vs 2st

t(66.84) = 1.28, p = 0.204, Cohen d = -0.34, 95% CI (-0.25 to 1.15)

ers_e

1st vs 2st

t(64.81) = -0.02, p = 0.982, Cohen d = 0.01, 95% CI (-0.41 to 0.41)

ers_r

1st vs 2st

t(69.72) = 0.51, p = 0.611, Cohen d = -0.13, 95% CI (-0.42 to 0.70)

pss_pa

1st vs 2st

t(69.82) = -1.47, p = 0.147, Cohen d = 0.38, 95% CI (-3.04 to 0.46)

pss_ps

1st vs 2st

t(67.10) = -0.12, p = 0.908, Cohen d = 0.03, 95% CI (-2.61 to 2.32)

pss

1st vs 2st

t(66.76) = 0.63, p = 0.533, Cohen d = -0.17, 95% CI (-2.47 to 4.74)

rki_responsible

1st vs 2st

t(68.33) = -0.52, p = 0.602, Cohen d = 0.14, 95% CI (-1.67 to 0.97)

rki_nonlinear

1st vs 2st

t(67.49) = 0.39, p = 0.699, Cohen d = -0.10, 95% CI (-0.78 to 1.15)

rki_peer

1st vs 2st

t(68.08) = 0.46, p = 0.649, Cohen d = -0.12, 95% CI (-0.61 to 0.98)

rki_expect

1st vs 2st

t(73.49) = 0.75, p = 0.453, Cohen d = -0.19, 95% CI (-0.26 to 0.58)

rki

1st vs 2st

t(68.86) = 0.15, p = 0.882, Cohen d = -0.04, 95% CI (-1.84 to 2.14)

raq_possible

1st vs 2st

t(69.68) = 0.98, p = 0.331, Cohen d = -0.26, 95% CI (-0.35 to 1.02)

raq_difficulty

1st vs 2st

t(66.92) = 0.82, p = 0.416, Cohen d = -0.22, 95% CI (-0.28 to 0.68)

raq

1st vs 2st

t(67.43) = 1.11, p = 0.270, Cohen d = -0.29, 95% CI (-0.45 to 1.59)

who

1st vs 2st

t(64.53) = -0.43, p = 0.666, Cohen d = 0.12, 95% CI (-1.51 to 0.97)

phq

1st vs 2st

t(62.74) = -0.02, p = 0.985, Cohen d = 0.01, 95% CI (-0.85 to 0.84)

gad

1st vs 2st

t(64.86) = 0.78, p = 0.441, Cohen d = -0.21, 95% CI (-0.56 to 1.27)

nb_pcs

1st vs 2st

t(63.71) = 1.76, p = 0.083, Cohen d = -0.47, 95% CI (-0.23 to 3.68)

nb_mcs

1st vs 2st

t(66.42) = -0.69, p = 0.490, Cohen d = 0.18, 95% CI (-3.64 to 1.76)

Within control group

sets

1st vs 2st

t(64.41) = -0.74, p = 0.461, Cohen d = 0.18, 95% CI (-1.09 to 0.50)

setv

1st vs 2st

t(62.66) = 0.97, p = 0.337, Cohen d = -0.23, 95% CI (-0.28 to 0.80)

maks

1st vs 2st

t(61.07) = 0.14, p = 0.892, Cohen d = -0.03, 95% CI (-0.92 to 1.06)

ibs

1st vs 2st

t(62.23) = 0.60, p = 0.553, Cohen d = -0.14, 95% CI (-0.44 to 0.82)

ers_e

1st vs 2st

t(61.36) = -2.87, p = 0.006, Cohen d = 0.69, 95% CI (-0.90 to -0.16)

ers_r

1st vs 2st

t(63.53) = -0.49, p = 0.626, Cohen d = 0.12, 95% CI (-0.64 to 0.39)

pss_pa

1st vs 2st

t(63.57) = -1.73, p = 0.089, Cohen d = 0.41, 95% CI (-2.99 to 0.22)

pss_ps

1st vs 2st

t(62.34) = 1.10, p = 0.277, Cohen d = -0.26, 95% CI (-1.01 to 3.47)

pss

1st vs 2st

t(62.19) = 1.62, p = 0.111, Cohen d = -0.39, 95% CI (-0.62 to 5.93)

rki_responsible

1st vs 2st

t(62.89) = 0.08, p = 0.938, Cohen d = -0.02, 95% CI (-1.16 to 1.25)

rki_nonlinear

1st vs 2st

t(62.52) = -0.77, p = 0.442, Cohen d = 0.19, 95% CI (-1.22 to 0.54)

rki_peer

1st vs 2st

t(62.78) = -0.10, p = 0.921, Cohen d = 0.02, 95% CI (-0.76 to 0.69)

rki_expect

1st vs 2st

t(65.40) = 0.83, p = 0.409, Cohen d = -0.20, 95% CI (-0.23 to 0.55)

rki

1st vs 2st

t(63.13) = -0.16, p = 0.872, Cohen d = 0.04, 95% CI (-1.97 to 1.67)

raq_possible

1st vs 2st

t(63.51) = -1.14, p = 0.258, Cohen d = 0.27, 95% CI (-0.99 to 0.27)

raq_difficulty

1st vs 2st

t(62.26) = -0.07, p = 0.944, Cohen d = 0.02, 95% CI (-0.45 to 0.42)

raq

1st vs 2st

t(62.49) = -0.75, p = 0.454, Cohen d = 0.18, 95% CI (-1.28 to 0.58)

who

1st vs 2st

t(61.24) = -0.56, p = 0.575, Cohen d = 0.14, 95% CI (-1.44 to 0.81)

phq

1st vs 2st

t(60.50) = 0.16, p = 0.877, Cohen d = -0.04, 95% CI (-0.70 to 0.82)

gad

1st vs 2st

t(61.38) = 0.51, p = 0.609, Cohen d = -0.12, 95% CI (-0.62 to 1.04)

nb_pcs

1st vs 2st

t(60.90) = -0.69, p = 0.493, Cohen d = 0.17, 95% CI (-2.37 to 1.15)

nb_mcs

1st vs 2st

t(62.05) = -0.17, p = 0.869, Cohen d = 0.04, 95% CI (-2.65 to 2.25)

Plot